Mining gene expression data by interpreting principal components

Background: There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis. Results: We present a method for automatically identifying such candidate sets of biologically relevant genes using a combination of principal components analysis and information theoretic metrics. To enable easy use of our methods, we have developed a data analysis package that fac...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Joseph C. Roden, Brandon W. King, Diane Trout, Ali Mortazavi, Barbara J. Wold, Christopher E. Hart
• Keywords: BMCBI 2006, Conditions, Microarray Expression Datasets, Relevant Genes